# Stock-Prediction-Models **Repository Path**: studvc/Stock-Prediction-Models ## Basic Information - **Project Name**: Stock-Prediction-Models - **Description**: Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2020-12-19 - **Last Updated**: 2020-12-19 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README

--- **Stock-Prediction-Models**, Gathers machine learning and deep learning models for Stock forecasting, included trading bots and simulations. ## Table of contents * [Models](https://github.com/huseinzol05/Stock-Prediction-Models#models) * [Agents](https://github.com/huseinzol05/Stock-Prediction-Models#agents) * [Realtime Agent](realtime-agent) * [Data Explorations](https://github.com/huseinzol05/Stock-Prediction-Models#data-explorations) * [Simulations](https://github.com/huseinzol05/Stock-Prediction-Models#simulations) * [Tensorflow-js](https://github.com/huseinzol05/Stock-Prediction-Models#tensorflow-js) * [Misc](https://github.com/huseinzol05/Stock-Prediction-Models#misc) * [Results](https://github.com/huseinzol05/Stock-Prediction-Models#results) * [Results Agent](https://github.com/huseinzol05/Stock-Prediction-Models#results-agent) * [Results signal prediction](https://github.com/huseinzol05/Stock-Prediction-Models#results-signal-prediction) * [Results analysis](https://github.com/huseinzol05/Stock-Prediction-Models#results-analysis) * [Results simulation](https://github.com/huseinzol05/Stock-Prediction-Models#results-simulation) ## Contents ### Models #### [Deep-learning models](deep-learning) 1. LSTM 2. LSTM Bidirectional 3. LSTM 2-Path 4. GRU 5. GRU Bidirectional 6. GRU 2-Path 7. Vanilla 8. Vanilla Bidirectional 9. Vanilla 2-Path 10. LSTM Seq2seq 11. LSTM Bidirectional Seq2seq 12. LSTM Seq2seq VAE 13. GRU Seq2seq 14. GRU Bidirectional Seq2seq 15. GRU Seq2seq VAE 16. Attention-is-all-you-Need 17. CNN-Seq2seq 18. Dilated-CNN-Seq2seq **Bonus** 1. How to use one of the model to forecast `t + N`, [how-to-forecast.ipynb](deep-learning/how-to-forecast.ipynb) 2. Consensus, how to use sentiment data to forecast `t + N`, [sentiment-consensus.ipynb](deep-learning/sentiment-consensus.ipynb) #### [Stacking models](stacking) 1. Deep Feed-forward Auto-Encoder Neural Network to reduce dimension + Deep Recurrent Neural Network + ARIMA + Extreme Boosting Gradient Regressor 2. Adaboost + Bagging + Extra Trees + Gradient Boosting + Random Forest + XGB ### [Agents](agent) 1. Turtle-trading agent 2. Moving-average agent 3. Signal rolling agent 4. Policy-gradient agent 5. Q-learning agent 6. Evolution-strategy agent 7. Double Q-learning agent 8. Recurrent Q-learning agent 9. Double Recurrent Q-learning agent 10. Duel Q-learning agent 11. Double Duel Q-learning agent 12. Duel Recurrent Q-learning agent 13. Double Duel Recurrent Q-learning agent 14. Actor-critic agent 15. Actor-critic Duel agent 16. Actor-critic Recurrent agent 17. Actor-critic Duel Recurrent agent 18. Curiosity Q-learning agent 19. Recurrent Curiosity Q-learning agent 20. Duel Curiosity Q-learning agent 21. Neuro-evolution agent 22. Neuro-evolution with Novelty search agent 23. ABCD strategy agent ### [Data Explorations](misc) 1. stock market study on TESLA stock, [tesla-study.ipynb](misc/tesla-study.ipynb) 2. Outliers study using K-means, SVM, and Gaussian on TESLA stock, [outliers.ipynb](misc/outliers.ipynb) 3. Overbought-Oversold study on TESLA stock, [overbought-oversold.ipynb](misc/overbought-oversold.ipynb) 4. Which stock you need to buy? [which-stock.ipynb](misc/which-stock.ipynb) ### [Simulations](simulation) 1. Simple Monte Carlo, [monte-carlo-drift.ipynb](simulation/monte-carlo-drift.ipynb) 2. Dynamic volatility Monte Carlo, [monte-carlo-dynamic-volatility.ipynb](simulation/monte-carlo-dynamic-volatility.ipynb) 3. Drift Monte Carlo, [monte-carlo-drift.ipynb](simulation/monte-carlo-drift.ipynb) 4. Multivariate Drift Monte Carlo BTC/USDT with Bitcurate sentiment, [multivariate-drift-monte-carlo.ipynb](simulation/multivariate-drift-monte-carlo.ipynb) 5. Portfolio optimization, [portfolio-optimization.ipynb](simulation/portfolio-optimization.ipynb), inspired from https://pythonforfinance.net/2017/01/21/investment-portfolio-optimisation-with-python/ ### [Tensorflow-js](stock-forecasting-js) I code [LSTM Recurrent Neural Network](deep-learning/1.lstm.ipynb) and [Simple signal rolling agent](agent/simple-agent.ipynb) inside Tensorflow JS, you can try it here, [huseinhouse.com/stock-forecasting-js](https://huseinhouse.com/stock-forecasting-js/), you can download any historical CSV and upload dynamically. ### [Misc](misc) 1. fashion trending prediction with cross-validation, [fashion-forecasting.ipynb](misc/fashion-forecasting.ipynb) 2. Bitcoin analysis with LSTM prediction, [bitcoin-analysis-lstm.ipynb](misc/bitcoin-analysis-lstm.ipynb) 3. Kijang Emas Bank Negara, [kijang-emas-bank-negara.ipynb](misc/kijang-emas-bank-negara.ipynb) ## Results ### Results Agent **This agent only able to buy or sell 1 unit per transaction.** 1. Turtle-trading agent, [turtle-agent.ipynb](agent/1.turtle-agent.ipynb)

2. Moving-average agent, [moving-average-agent.ipynb](agent/2.moving-average-agent.ipynb)

3. Signal rolling agent, [signal-rolling-agent.ipynb](agent/3.signal-rolling-agent.ipynb)

4. Policy-gradient agent, [policy-gradient-agent.ipynb](agent/4.policy-gradient-agent.ipynb)

5. Q-learning agent, [q-learning-agent.ipynb](agent/5.q-learning-agent.ipynb)

6. Evolution-strategy agent, [evolution-strategy-agent.ipynb](agent/6.evolution-strategy-agent.ipynb)

7. Double Q-learning agent, [double-q-learning-agent.ipynb](agent/7.double-q-learning-agent.ipynb)

8. Recurrent Q-learning agent, [recurrent-q-learning-agent.ipynb](agent/8.recurrent-q-learning-agent.ipynb)

9. Double Recurrent Q-learning agent, [double-recurrent-q-learning-agent.ipynb](agent/9.double-recurrent-q-learning-agent.ipynb)

10. Duel Q-learning agent, [duel-q-learning-agent.ipynb](agent/10.duel-q-learning-agent.ipynb)

11. Double Duel Q-learning agent, [double-duel-q-learning-agent.ipynb](agent/11.double-duel-q-learning-agent.ipynb)

12. Duel Recurrent Q-learning agent, [duel-recurrent-q-learning-agent.ipynb](agent/12.duel-recurrent-q-learning-agent.ipynb)

13. Double Duel Recurrent Q-learning agent, [double-duel-recurrent-q-learning-agent.ipynb](agent/13.double-duel-recurrent-q-learning-agent.ipynb)

14. Actor-critic agent, [actor-critic-agent.ipynb](agent/14.actor-critic-agent.ipynb)

15. Actor-critic Duel agent, [actor-critic-duel-agent.ipynb](agent/14.actor-critic-duel-agent.ipynb)

16. Actor-critic Recurrent agent, [actor-critic-recurrent-agent.ipynb](agent/16.actor-critic-recurrent-agent.ipynb)

17. Actor-critic Duel Recurrent agent, [actor-critic-duel-recurrent-agent.ipynb](agent/17.actor-critic-duel-recurrent-agent.ipynb)

18. Curiosity Q-learning agent, [curiosity-q-learning-agent.ipynb](agent/18.curiosity-q-learning-agent.ipynb)

19. Recurrent Curiosity Q-learning agent, [recurrent-curiosity-q-learning.ipynb](agent/19.recurrent-curiosity-q-learning-agent.ipynb)

20. Duel Curiosity Q-learning agent, [duel-curiosity-q-learning-agent.ipynb](agent/20.duel-curiosity-q-learning-agent.ipynb)

21. Neuro-evolution agent, [neuro-evolution.ipynb](agent/21.neuro-evolution-agent.ipynb)

22. Neuro-evolution with Novelty search agent, [neuro-evolution-novelty-search.ipynb](agent/22.neuro-evolution-novelty-search-agent.ipynb)

23. ABCD strategy agent, [abcd-strategy.ipynb](agent/23.abcd-strategy-agent.ipynb)

### Results signal prediction I will cut the dataset to train and test datasets, 1. Train dataset derived from starting timestamp until last 30 days 2. Test dataset derived from last 30 days until end of the dataset So we will let the model do forecasting based on last 30 days, and we will going to repeat the experiment for 10 times. You can increase it locally if you want, and tuning parameters will help you by a lot. 1. LSTM, accuracy 95.693%, time taken for 1 epoch 01:09